Developing a New Security Framework for Bluetooth Low Energy Devices

Wearable devices are becoming more popular in our daily life. They are usually used to monitor health status, track fitness data, or even do medical tests, etc. Since the wearable devices can obtain a lot of personal data, their security issues are very important. Motivated by the consideration that the current pairing mechanisms of Bluetooth Low Energy (BLE) are commonly impractical or insecure for many BLE based wearable devices nowadays, we design and implement a security framework in order to protect the communication between these devices. The security framework is a supplement to the Bluetooth pairing mechanisms and is compatible with all BLE based wearable devices. The framework is a module between the application layer and the GATT (Generic Attribute Profile) layer in the BLE architecture stack. When the framework starts, a client and a server can automatically and securely establish shared fresh keys following a designed protocol; the services of encrypting and decrypting messages are provided to the applications conveniently by two functions; application data are securely transmitted following another protocol using the generated keys. Prudential principles are followed by the design of the framework for security purposes. It can protect BLE based wearable devices from replay attacks, Man-in-The-Middle attacks, data tampering, and passive eavesdropping. We conduct experiments to show that the framework can be conveniently deployed with practical operational cost of power consumption. The protocols in this framework have been formally verified that the designed security goals are satisfied

Feature Selection Method Based on Class Discriminative Degree for Intelligent Medical Diagnosis

By using efficient and timely medical diagnostic decision making, clinicians can positively impact the quality and cost of medical care. However, the high similarity of clinical manifestations between diseases and the limitation of clinicians’ knowledge both bring much difficulty to decision making in diagnosis. Therefore, building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain. In this paper, we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories, and compare this method with the traditional medical expert system to verify the performance. To select the best subset of patient features, we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test. We evaluate the feature selection methods and diagnostic models from two aspects, false negative rate (FNR) and accuracy. Extensive experiments have conducted on a real-world Chinese electronic medical record database. The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods, and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system

Modifying AI, Enhancing Essays:How Active Engagement with Generative AI Boosts Writing Quality

Students are increasingly relying on Generative AI (GAI) to support their writing - a key pedagogical practice in education. In GAI-assisted writing, students can delegate core cognitive tasks (e.g., generating ideas and turning them into sentences) to GAI while still producing high-quality essays. This creates new challenges for teachers in assessing and supporting student learning, as they often lack insight into whether students are engaging in meaningful cognitive processes during writing or how much of the essay's quality can be attributed to those processes. This study aimed to help teachers better assess and support student learning in GAI-assisted writing by examining how different writing behaviors, especially those indicative of meaningful learning versus those that are not, impact essay quality. Using a dataset of 1,445 GAI-assisted writing sessions, we applied the cutting-edge method, X-Learner, to quantify the causal impact of three GAI-assisted writing behavioral patterns (i.e., seeking suggestions but not accepting them, seeking suggestions and accepting them as they are, and seeking suggestions and accepting them with modification) on four measures of essay quality (i.e., lexical sophistication, syntactic complexity, text cohesion, and linguistic bias). Our analysis showed that writers who frequently modified GAI-generated text - suggesting active engagement in higher-order cognitive processes - consistently improved the quality of their essays in terms of lexical sophistication, syntactic complexity, and text cohesion. In contrast, those who often accepted GAI-generated text without changes, primarily engaging in lower-order processes, saw a decrease in essay quality. Additionally, while human writers tend to introduce linguistic bias when writing independently, incorporating GAI-generated text - even without modification - can help mitigate this bias.</p

Temperature and strain-rate dependence of the superelastic response in EBF3-fabricated and post-heat-treated NiTi shape memory alloy

This work was supported by the National Key Research and Development Program of China (Grant no. 2022YFF0609000 ), National Natural Science Foundation of China (Grant no. 51871075 and 52171034 ), and the Foundation of National Key Laboratory for Precision Hot Processing of Metals ( JCKYS2022603C003 ). Publisher Copyright: © 2024 The AuthorsIn the present work, the temperature and strain-rate dependence of the superelastic response behavior of NiTi shape memory alloys prepared via electron beam freeform fabrication (EBF3) technique and post-heat-treated is systematically studied. It is demonstrated that the response hysteresis of the EBF3-fabricated NiTi alloys during superelastic cycling decreases with increasing strain-rate at the same test temperature. However, the superelastic response of material gradually alters to a nearly linear mode with the increase of test temperature under the same strain-rate. The corresponding stable superelasticity recovery ratio decreases from 62.6 % to 47.6 % when the test temperature improves from 100 °C to 150 °C. This is mainly due to the activation of dislocations in the {011} slip system of B2 austenite, in which they become plugged and entangled during plastic deformation, deteriorating the material superelastic recovery ability. These findings provide a selection basis for the service conditions of the rapidly prepared NiTi alloys under EBF3-fabrication and post-heat-treatment technology routes.publishersversionpublishe

Development of virtual reality support to factory layout planning

Virtual reality (VR) technology has become ever mature today with affordable and yet powerful hardware. In the manufacturing industry, there is a growing interest of adopting VR to improve existing work procedures. Factory layout planning (FLP) is a long standing area in production engineering that sees great potentials of VR integration. Virtual reality supported layout planning (VLP) is gaining wider attention in research and practice as the virtual environment allows designers to test out “what if” scenarios in relative ease. However, previous research of VLP mostly focus on general layout planning but not the detailed level planning. Also, it is reported that the virtual modeling process is time-consuming and costly. In this study, we propose a point cloud based virtual factory modelling approach for the VLP tasks. It incorporates point cloud representation of physical environment with CAD data to model the virtual factory with the aims of simplifying the modelling process and improving decision-making for the VLP tasks. The proposed approach is exemplified and refined through three industrial cases. The implementations and results of the cases are highlighted and discussed in details. At the end, a general guidance for VLP is extracted and presented for future point cloud based VR support in FLP tasks